Add full probability distributions to dashboard

frontend/components/dashboard/PanelSections.tsx

@@ -858,6 +858,10 @@ export function ProbabilityDistribution({
     ? formatBucketDisplayLabel(topProbability, detail)
     : null;
   const topProbabilityTemp = topProbability ? getBucketTemp(topProbability) : null;
+  const probabilitiesForMarketContracts =
+    view.probabilitiesAll?.length > 0
+      ? view.probabilitiesAll
+      : view.probabilities || [];
   const marketContractRows = useMemo<ProbabilityDisplayRow[]>(() => {
     if (!isToday || !marketScan?.available || marketAllBuckets.length === 0) {
       return [];
@@ -867,7 +871,7 @@ export function ProbabilityDistribution({
     const seenKeys = new Set<string>();
     for (const marketBucket of marketAllBuckets) {
       const probability = getAggregatedModelProbabilityForMarketBucket(
-        view.probabilities || [],
+        probabilitiesForMarketContracts,
         marketBucket,
         detail,
       );
@@ -886,7 +890,13 @@ export function ProbabilityDistribution({
       });
     }
     return rows;
-  }, [detail, isToday, marketAllBuckets, marketScan?.available, view.probabilities]);
+  }, [
+    detail,
+    isToday,
+    marketAllBuckets,
+    marketScan?.available,
+    probabilitiesForMarketContracts,
+  ]);
   const modelProbabilityRows = useMemo<ProbabilityDisplayRow[]>(
     () =>
       (view.probabilities || []).slice(0, 6).map((bucket, index) => {
@@ -938,7 +948,7 @@ export function ProbabilityDistribution({
     topProbabilityLabel ||
     null;
   const aggregatedMarketProbability = getAggregatedModelProbabilityForMarketBucket(
-    view.probabilities || [],
+    probabilitiesForMarketContracts,
     linkedMarketBucket,
     detail,
   );

frontend/lib/dashboard-types.ts

@@ -488,6 +488,7 @@ export interface CityDetail {
   probabilities?: {
     mu?: number | null;
     distribution?: ProbabilityBucket[];
+    distribution_all?: ProbabilityBucket[];
     engine?: string | null;
     calibration_mode?: string | null;
     calibration_version?: string | null;
@@ -496,6 +497,7 @@ export interface CityDetail {
     calibrated_mu?: number | null;
     calibrated_sigma?: number | null;
     shadow_distribution?: ProbabilityBucket[];
+    shadow_distribution_all?: ProbabilityBucket[];
   };
   hourly?: {
     times?: string[];

frontend/lib/dashboard-utils.ts

@@ -1212,7 +1212,15 @@ export function getProbabilityView(detail: CityDetail, targetDate?: string | nul
       engine: detail.probabilities?.engine ?? null,
       mu: detail.probabilities?.mu ?? null,
       probabilities: detail.probabilities?.distribution || [],
+      probabilitiesAll:
+        detail.probabilities?.distribution_all ||
+        detail.probabilities?.distribution ||
+        [],
       shadowProbabilities: detail.probabilities?.shadow_distribution || [],
+      shadowProbabilitiesAll:
+        detail.probabilities?.shadow_distribution_all ||
+        detail.probabilities?.shadow_distribution ||
+        [],
     };
   }
 
@@ -1223,7 +1231,9 @@ export function getProbabilityView(detail: CityDetail, targetDate?: string | nul
     engine: null,
     mu: daily?.deb?.prediction ?? null,
     probabilities: daily?.probabilities || [],
+    probabilitiesAll: daily?.probabilities || [],
     shadowProbabilities: [],
+    shadowProbabilitiesAll: [],
   };
 }
 

src/analysis/probability_calibration.py

@@ -169,7 +169,7 @@ def _bucket_probabilities(
     sigma: float,
     max_so_far: Optional[float],
     city_name: str,
-) -> Tuple[List[Dict[str, Any]], List[Tuple[int, float]]]:
+) -> Tuple[List[Dict[str, Any]], List[Tuple[int, float]], List[Dict[str, Any]]]:
     sigma = max(0.1, float(sigma))
     min_possible = (
         apply_city_settlement(city_name, max_so_far) if max_so_far is not None else -999
@@ -193,24 +193,24 @@ def _bucket_probabilities(
 
     total = sum(probs.values())
     if total <= 0:
-        return [], []
+        return [], [], []
 
     normalized = {key: val / total for key, val in probs.items()}
     sorted_probs = sorted(normalized.items(), key=lambda item: item[1], reverse=True)
-    distribution = []
+    full_distribution = []
-    for value, prob in sorted_probs[:4]:
+    for value, prob in sorted_probs:
         if is_exact:
             bucket_range = "[{0}.0~{1}.0)".format(value, value + 1)
         else:
             bucket_range = "[{0}~{1})".format(value - 0.5, value + 0.5)
-        distribution.append(
+        full_distribution.append(
             {
                 "value": int(value),
                 "range": bucket_range,
                 "probability": round(prob, 3),
             }
         )
-    return distribution, sorted_probs
+    return full_distribution[:4], sorted_probs, full_distribution
 
 
 def _top_bucket_value(distribution: Optional[List[Dict[str, Any]]]) -> Optional[int]:
@@ -299,7 +299,9 @@ def apply_probability_calibration(
             "mode": selected_mode,
             "engine": ENGINE_MODE_LEGACY,
             "distribution": legacy_distribution or [],
+            "distribution_all": legacy_distribution or [],
             "shadow_distribution": [],
+            "shadow_distribution_all": [],
             "raw_mu": raw_mu,
             "raw_sigma": raw_sigma,
             "calibrated_mu": None,
@@ -314,7 +316,9 @@ def apply_probability_calibration(
             "mode": selected_mode,
             "engine": ENGINE_MODE_LEGACY,
             "distribution": legacy_distribution or [],
+            "distribution_all": legacy_distribution or [],
             "shadow_distribution": [],
+            "shadow_distribution_all": [],
             "raw_mu": raw_mu,
             "raw_sigma": raw_sigma,
             "calibrated_mu": None,
@@ -384,7 +388,7 @@ def apply_probability_calibration(
             calibrated_mu = observed_floor
     calibrated_sigma = max(0.1, _blend_value(raw_sigma, calibrated_sigma, blend_alpha_sigma))
     calibrated_sigma = _clamp_sigma(raw_sigma, calibrated_sigma, sigma_constraints)
-    calibrated_distribution, calibrated_sorted = _bucket_probabilities(
+    calibrated_distribution, calibrated_sorted, calibrated_distribution_all = _bucket_probabilities(
         calibrated_mu,
         calibrated_sigma,
         max_so_far=max_so_far,
@@ -393,23 +397,29 @@ def apply_probability_calibration(
 
     engine = ENGINE_MODE_LEGACY
     selected_distribution = legacy_distribution or []
+    selected_distribution_all = legacy_distribution or []
     selected_sorted: List[Tuple[int, float]] = []
     shadow_distribution: List[Dict[str, Any]] = []
+    shadow_distribution_all: List[Dict[str, Any]] = []
     shadow_sorted: List[Tuple[int, float]] = []
     if selected_mode == ENGINE_MODE_EMOS_PRIMARY:
         engine = "emos"
         selected_distribution = calibrated_distribution
+        selected_distribution_all = calibrated_distribution_all
         selected_sorted = calibrated_sorted
     elif selected_mode == ENGINE_MODE_EMOS_SHADOW:
         shadow_distribution = calibrated_distribution
+        shadow_distribution_all = calibrated_distribution_all
         shadow_sorted = calibrated_sorted
 
     return {
         "mode": selected_mode,
         "engine": engine,
         "distribution": selected_distribution,
+        "distribution_all": selected_distribution_all,
         "selected_sorted_probs": selected_sorted,
         "shadow_distribution": shadow_distribution,
+        "shadow_distribution_all": shadow_distribution_all,
         "shadow_sorted_probs": shadow_sorted,
         "raw_mu": raw_mu,
         "raw_sigma": raw_sigma,
@@ -551,7 +561,7 @@ def fit_calibration(
             == apply_city_settlement(city, actual_high)
             else 0.0
         )
-        legacy_distribution, _ = _bucket_probabilities(
+        legacy_distribution, _, _ = _bucket_probabilities(
             legacy_mu,
             legacy_sigma,
             max_so_far=None,
@@ -627,7 +637,7 @@ def fit_calibration(
                 city = row["city"]
                 crps_values.append(_gaussian_crps(actual_high, mu_hat, sigma_hat))
                 mae_values.append(abs(mu_hat - actual_high))
-                distribution, _ = _bucket_probabilities(
+                distribution, _, _ = _bucket_probabilities(
                     mu_hat,
                     sigma_hat,
                     max_so_far=None,

src/analysis/trend_engine.py

@@ -594,7 +594,9 @@ def analyze_weather_trend(
 
     # === Probability Engine ===
     probabilities: List[Dict[str, Any]] = []
+    probabilities_all: List[Dict[str, Any]] = []
     shadow_probabilities: List[Dict[str, Any]] = []
+    shadow_probabilities_all: List[Dict[str, Any]] = []
     forecast_miss_deg = 0.0
     probability_features = None
     calibration_summary = {
@@ -621,6 +623,7 @@ def analyze_weather_trend(
             probabilities = [
                 {"value": settled_wu, "range": f"[{settled_wu-0.5}~{settled_wu+0.5})", "probability": 1.0}
             ]
+            probabilities_all = probabilities
     elif (ens_p10 is not None and ens_p90 is not None) or fallback_sigma:
         # Forecast miss magnitude
         if max_so_far is not None and forecast_median is not None:
@@ -665,6 +668,7 @@ def analyze_weather_trend(
         )
         mu = probs_result.get("mu", mu)
         probabilities = probs_result.get("probabilities", [])
+        probabilities_all = probs_result.get("probabilities_all", probabilities)
         sorted_probs = probs_result.get("sorted_probs", [])
 
         probability_features = build_probability_features(
@@ -698,10 +702,12 @@ def analyze_weather_trend(
             "calibration_source": calibration_result.get("calibration_source"),
         }
         shadow_probabilities = calibration_result.get("shadow_distribution") or []
+        shadow_probabilities_all = calibration_result.get("shadow_distribution_all") or shadow_probabilities
         if calibration_result.get("engine") == "emos":
             mu = calibration_result.get("calibrated_mu", mu)
             sigma = calibration_result.get("calibrated_sigma", sigma)
             probabilities = calibration_result.get("distribution") or probabilities
+            probabilities_all = calibration_result.get("distribution_all") or probabilities_all or probabilities
             sorted_probs = calibration_result.get("selected_sorted_probs") or sorted_probs
 
         if sorted_probs:
@@ -926,7 +932,9 @@ def analyze_weather_trend(
     structured = {
         "mu": mu,
         "probabilities": probabilities,
+        "probabilities_all": probabilities_all or probabilities,
         "shadow_probabilities": shadow_probabilities,
+        "shadow_probabilities_all": shadow_probabilities_all or shadow_probabilities,
         "probability_engine": calibration_summary["engine"],
         "probability_calibration_mode": calibration_summary["mode"],
         "probability_calibration_version": calibration_summary["calibration_version"],
@@ -999,21 +1007,24 @@ def calculate_prob_distribution(
     total_p = sum(probs.values())
     sorted_probs = []
     probabilities = []
+    probabilities_all = []
     
     if total_p > 0:
         norm_probs = {k: v / total_p for k, v in probs.items()}
         sorted_probs = sorted(norm_probs.items(), key=lambda x: x[1], reverse=True)
-        for t, p in sorted_probs[:4]:
+        for t, p in sorted_probs:
             rng_str = f"[{t}.0~{t+1}.0)" if is_exact else f"[{t-0.5}~{t+0.5})"
-            probabilities.append({
+            probabilities_all.append({
                 "value": int(t),
                 "range": rng_str,
                 "probability": round(p, 3)
             })
+        probabilities = probabilities_all[:4]
 
     return {
         "mu": mu,
         "sigma": sigma,
         "probabilities": probabilities,
+        "probabilities_all": probabilities_all,
         "sorted_probs": sorted_probs
     }

tests/test_probability_calibration.py

@@ -96,6 +96,7 @@ def test_shadow_mode_keeps_legacy_distribution(tmp_path):
     assert result["shadow_distribution"]
     assert result["calibrated_mu"] == 10.5
     assert result["calibrated_sigma"] == 1.2
+    assert len(result["shadow_distribution_all"]) >= len(result["shadow_distribution"])
 
 
 def test_primary_mode_switches_to_calibrated_distribution(tmp_path):
@@ -129,6 +130,7 @@ def test_primary_mode_switches_to_calibrated_distribution(tmp_path):
     assert result["calibrated_mu"] == 10.5
     assert result["calibrated_sigma"] == 1.2
     assert result["distribution"]
+    assert len(result["distribution_all"]) >= len(result["distribution"])
     assert result["distribution"][0]["value"] >= 10
 
 

web/analysis_service.py

@@ -2006,7 +2006,9 @@ def _analyze(
     from src.analysis.trend_engine import analyze_weather_trend as _trend_analyze, calculate_prob_distribution
 
     probabilities = []
+    probabilities_all = []
     shadow_probabilities = []
+    shadow_probabilities_all = []
     mu = None
     probability_engine = "legacy"
     probability_calibration_mode = "legacy"
@@ -2022,7 +2024,9 @@ def _analyze(
         # Use structured data from shared engine
         mu = sd.get("mu")
         probabilities = sd.get("probabilities", [])
+        probabilities_all = sd.get("probabilities_all", probabilities)
         shadow_probabilities = sd.get("shadow_probabilities", [])
+        shadow_probabilities_all = sd.get("shadow_probabilities_all", shadow_probabilities)
         probability_engine = sd.get("probability_engine", "legacy")
         probability_calibration_mode = sd.get("probability_calibration_mode", "legacy")
         probability_calibration_version = sd.get("probability_calibration_version")
@@ -2406,6 +2410,7 @@ def _analyze(
         "probabilities": {
             "mu": round(mu, 1) if mu is not None else None,
             "distribution": probabilities,
+            "distribution_all": probabilities_all or probabilities,
             "engine": probability_engine,
             "calibration_mode": probability_calibration_mode,
             "calibration_version": probability_calibration_version,
@@ -2414,6 +2419,7 @@ def _analyze(
             "calibrated_mu": probability_calibrated_mu,
             "calibrated_sigma": probability_calibrated_sigma,
             "shadow_distribution": shadow_probabilities,
+            "shadow_distribution_all": shadow_probabilities_all or shadow_probabilities,
         },
         "trend": trend_info,
         "peak": {